ArticleNature methods2024
TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 30 citations in OpenAlex.
- Article
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Counterfactual Diffusion Modeling Enables Spatially Targeted Reprogramming of Tissue Microenvironments.Biology · 2026Article
- R4ST: a reference-guided graph-generative model for robust reconstruction of spatial transcriptomic profiles.Bioinformatics (Oxford, England) · 2026Article
- Implementing trust in non-small cell lung cancer diagnosis with a conformalized uncertainty-aware AI framework.Nature biomedical engineering · 2026Article
- Article
- Lifelong behavioral screen reveals an architecture of vertebrate aging.Science (New York, N.Y.) · 2026Article
- Spatial transcriptomics and artificial intelligence: a scoping review of emerging applications in head and neck pathology.Head and neck pathology · 2026Article
- Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.Nature methods · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- VISTA uncovers missing gene expression and spatial-induced information for spatial transcriptomic data analysis.Communications biology · 2026Article
- SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.Computational and structural biotechnology journal · 2026Article
- SpatialFinder: a human-in-the-loop vision-language framework for prioritizing high-value regions in spatial transcriptomics.Frontiers in bioinformatics · 2026Article
- Defining the role of natural killer cells in acute myeloid leukemia through the lens of single-cell omics.Frontiers in immunology · 2026Review
- Accurate imputation of pathway-specific gene expression in spatial transcriptomics with PASTA.Nature communications · 2025Article
- SpatialProp: tissue perturbation modeling with spatially resolved single-cell transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Life-long behavioral screen reveals an architecture of vertebrate aging.bioRxiv : the preprint server for biology · 2025Article
- VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis.Research square · 2025Article
- Conformal inference for reliable single cell RNA-seq annotation.Bioinformatics (Oxford, England) · 2025Article
- Computational pathology annotation enhances the resolution and interpretation of breast cancer spatial transcriptomics data.NPJ precision oncology · 2025Article
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Authors and funding
5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.
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